Custom Prediction Models for Player Props
- Greg Kajewski
- Jan 15
- 13 min read
Player props betting focuses on individual player performance, such as points, rebounds, or assists, rather than game outcomes. These bets are less scrutinized by sportsbooks, creating opportunities for bettors who use data-driven strategies. To succeed, you need a model that identifies value in the odds and reduces bias.
Key steps include:
Tracking metrics like usage rate, target share, and pace of play.
Adjusting for game context, such as matchups, injuries, and home/away splits.
Building a prediction model using historical data and engineered features.
Testing and refining your model with tools like BettorEdge, which offers analytics, bet tracking, and community insights.
Build an NBA Player Prop Model in Excel: Predict Points, Rebounds, & Assists!
Key Metrics for Player Prop Predictions
Creating accurate prediction models starts with focusing on the right data. The metrics you choose will determine whether your projections align with reality or get muddled by small-sample noise and public betting biases.
Performance Statistics
The foundation of any model lies in core stats like points, rebounds, assists, yards, and touchdowns. However, metrics such as usage rate and target share often provide better predictive power. For instance, a wide receiver commanding 39% of his team's targets offers a more reliable baseline than one relying on touchdowns from limited opportunities. In the NFL, quarterback passing yards and attempts tend to follow consistent patterns, while reception data can vary widely between positions like running backs and wide receivers.
Don’t overvalue short-term performance. A 10-game sample comes with a ±31% error margin, making it unreliable for predictions. To reduce this margin to around ±6.9%, you’d need data from approximately 200 games. Using median season data helps smooth out short-term fluctuations and minimizes the impact of outliers.
Once you’ve gathered these metrics, it’s crucial to adjust them based on game-specific factors.
Game Context Factors
Matchups can significantly alter a player’s performance potential. For example, analyzing defensive rankings against specific positions - like how a defense fares against slot receivers versus outside receivers - can reveal valuable insights. In the 2021-22 NBA season, Anthony Edwards showcased notable home/away splits, averaging 18.7 points per game on 41.3% shooting at home, but improving to 23.6 points on 46.4% shooting on the road. Other contextual factors, such as back-to-back games, home-court advantage, and minutes played, are equally important.
When a key player is sidelined, the ripple effect can create opportunities for others to step up. Filtering historical data to isolate games played under specific conditions - like "games without [Teammate Name]" - can result in more precise projections. Additionally, monitoring line movement can reveal sharp betting trends that reflect informed opinions.
To fine-tune your predictions, external factors also need to be considered.
External Influences
Pace of play is a critical factor for predicting volume stats. Faster-paced games result in more possessions, increasing opportunities for players to rack up points, rebounds, or passing attempts. For example, an analysis of over 12,000 NFL prop bets across three seasons found that "under" bets hit 56% of the time, as sportsbooks often shade lines toward the "over" to align with public betting tendencies.
Blowout scenarios also impact player stats. When the point spread is greater than 11 points, starters may lose around 12 minutes of playing time, which can significantly affect their production. Lastly, always compare lines across multiple sportsbooks. Even a seemingly small difference - like 2 yards on an NFL receiving prop - can have a meaningful impact on your long-term return on investment.
How to Build a Custom Prediction Model
Creating a prediction model involves a series of steps that transform raw data into insights you can use for smarter betting decisions.
Step 1: Collect and Clean Data
Start by gathering historical player stats from sports data APIs or open databases. Aim for at least 200 games per player to keep your confidence interval at ±6.9%. Using fewer than 50 games increases uncertainty to ±13.9%, making predictions unreliable.
Consistency in data matters. Standardize team names (e.g., "LA Rams" vs. "Los Angeles Rams"), date formats, and headers across all sources. This ensures your model can properly integrate the data.
Avoid "look-ahead leakage" by sticking to pre-game data. For example, if you're projecting stats for a Friday game, don’t include Sunday injury reports in your training set. Such errors create false accuracy that won’t hold up in real-world scenarios. Rolling averages from the last 6–8 weeks often provide a better picture of current performance than full-season stats.
Once your data is clean and standardized, you’re ready to create predictive features.
Step 2: Engineer Features
Raw stats only tell part of the story. To add depth, create new variables that highlight context and efficiency. For example:
Usage rate: Percentage of team plays involving a specific player.
Target share: Proportion of passing plays aimed at a receiver.
Pace-adjusted metrics: Adjust stats for the speed at which teams play.
Incorporate matchup-specific adjustments, like defensive rankings against certain positions. For instance, if a running back faces a defense ranked 28th against the run, that’s a key feature. Don’t forget secondary effects - if a star player is out, adjust projections for teammates who will likely step up.
Advanced metrics, such as Expected Points Added (EPA) and Completion Percentage Over Expectation (CPOE), dig deeper into efficiency than basic box scores. Environmental factors, like wind speeds over 15 mph, travel fatigue, or playing on turf versus grass, also impact performance.
These engineered features lay the foundation for building a more accurate model.
Step 3: Choose and Train a Model
Start simple. Tools like Excel or Google Sheets work well for linear regression. As your skills grow, explore Python libraries like Scikit-learn and Pandas for more advanced machine learning techniques like decision trees.
If coding feels daunting, consider no-code platforms like Rithmm. These services offer pre-built data and AI-driven modeling, making it easier to get started. The key is choosing tools that match your experience level rather than diving into complex algorithms you don’t fully understand.
"The goal is to create a model that uses data/stats to identify profitable betting opportunities that takes out all biases." - OddsJam
When training your model, use probabilistic methods. For example, convert point spreads into win probabilities with logistic transforms or use Poisson regression for totals. Be cautious of overfitting - weight long-term career data more heavily than recent hot streaks, which may just reflect random variance.
Once trained, your model needs thorough testing and validation to ensure it holds up under real-world conditions.
Step 4: Validate and Test the Model
Use walk-forward validation to simulate real-time betting. Train your model on weeks 1–8, test it on week 9, then roll forward. This approach better reflects live betting scenarios compared to testing on your entire historical dataset.
Backtest your model on unseen games to identify Positive Expected Value (+EV) opportunities. Track metrics like Closing Line Value (CLV) - a measure of whether your model’s predicted odds beat the final market lines - as a key indicator of success. Use tools like Brier scores or log loss functions to evaluate how well your predicted probabilities align with actual outcomes.
For a reliable strategy, you’ll need a sample size of over 1,000 bets. This patience during validation helps avoid chasing false edges.
Step 5: Use the Model for Betting
Calculate Expected Value (EV) using this formula: (Probability of Winning × Profit) – (Probability of Losing). Only place bets when EV is greater than zero. Keep in mind that breaking even on standard -110 odds requires a true win probability of at least 52.4%.
Before comparing your model’s predictions to market odds, account for the sportsbook’s commission (vig) to find the "fair" probability. Then shop around for the best lines - a small difference, like 2 yards on a receiving prop, can have a big impact on your long-term ROI.
Use BettorEdge to monitor your model’s performance in real-world scenarios. Their analytics can break down results by league, bet type, and time period, helping you identify what’s working and what needs tweaking. You can also compare your model’s predictions with community insights to spot blind spots or confirm your edge before committing more of your bankroll.
"Expected value is the mathematically rigorous way to evaluate any bet... It tells you, on average over many repetitions, how much you stand to gain or lose per dollar wagered." - Joey Shackelford, Wizard of Odds
Finally, apply the Kelly Criterion to determine the size of your bets. This formula balances your edge against your bankroll, helping you minimize risk during inevitable losing streaks. By sticking to this disciplined approach, you can protect your capital while maximizing long-term gains.
Use BettorEdge’s tools to continually refine your model and uncover profitable opportunities.
Tools for Creating Prediction Models
The tools you choose can mean the difference between spending countless hours wrestling with spreadsheets and crafting a functional model in just a weekend. Your selection depends on your technical skills and how deep into the process you want to dive. From coding-heavy statistical tools to beginner-friendly no-code platforms, there’s something for every skill level.
Statistical Tools and Programming
Excel and Google Sheets are great starting points. They’re free, easy to use, and perfect for tasks like tracking player stats, calculating rolling averages, or testing formulas - all without writing a single line of code. But as your data grows beyond a few hundred rows, spreadsheets can become sluggish and overly manual.
If you’re ready for something more advanced, Python is a powerful option. With libraries like Pandas and NumPy, you can handle data manipulation and computation effortlessly. Add Scikit-learn to the mix, and you’ve got machine learning algorithms at your fingertips. R is another excellent choice, particularly for statistical analysis, thanks to packages like tidyverse and caret. These tools require some effort to learn, but they save you time in the long run - allowing you to scrape data from APIs, clean thousands of rows in seconds, and run simulations that would take days in Excel.
For visualizing your results, tools like Tableau and Power BI make it easy to spot trends and track performance. If you’re dealing with larger datasets, PostgreSQL can handle database management efficiently. And don’t forget to use version control systems and code editors to keep your projects organized.
"A betting model... is a predictive engine you build from the ground up. It doesn't just look at past results; it uses data to understand the factors that cause those results." - NXTbets
No-Code Platforms for Beginners
Not ready to dive into coding? No problem. No-code platforms can handle much of the heavy lifting for you. These tools offer pre-built data pipelines, automated cleaning, and AI-powered modeling through a user-friendly interface. You can tweak metrics and fine-tune variables without ever touching code. They’re especially handy for backtesting strategies, refining projections, and tracking results - all in a fraction of the time it would take to build from scratch.
With the U.S. sports betting market valued at nearly $18 billion in 2024 and projected to surpass $33 billion by 2030, investing in the right tools now could set you up for success as the market continues to grow.
Using BettorEdge for Model Validation
Once you’ve built your model - whether through programming or a no-code platform - it’s crucial to validate its accuracy. This is where BettorEdge comes in. By leveraging real market data, you can benchmark and refine your projections. Compare your custom predictions against live market odds to uncover value gaps and identify mispriced props. BettorEdge’s advanced analytics can break down your return on investment by bet type, track win rates by sport, and monitor prediction consistency over time. This helps you zero in on which player prop categories your model predicts most effectively.
Take advantage of the community leaderboards to measure your 30-day win percentage against top users. If your model isn’t performing as well as the community average, it might be time to revisit your features or data sources. The platform’s social feed is another valuable resource, offering insights into trends - like locker room dynamics or referee tendencies - that your model might not account for.
Finally, BettorEdge’s exchange and market liquidity tools allow you to set your own odds and compare them with community-driven pricing. This transparency helps you fine-tune bet sizing and identify specific markets where your model truly excels. With everything in one place, you can learn from others, iterate quickly, and improve your results far faster than working alone.
Improving Models with BettorEdge Analytics
BettorEdge's analytics tools take your player prop prediction model to the next level. Once you've built your model, you can test it against live market data and fine-tune it based on real-world outcomes. These tools transform your betting history into actionable insights, helping you pinpoint where your model shines and where it could use some work. By integrating seamlessly into your workflow, BettorEdge's analytics ensure your decisions stay rooted in data.
Tracking Performance Metrics
BettorEdge provides detailed reporting tools that break down your performance by league, bet type, and time period. This gives you a clear picture of where your model excels. For example, if your NBA points predictions hit at 58% but your NFL receiving yards bets hover around 48%, it's a sign your NFL feature engineering might need some tweaking.
You can also track your ROI across different types of props to uncover your model's strengths. If your over/under predictions on player assists consistently deliver an 8% ROI, but your touchdown props are dragging at -3%, you know where to focus your efforts. Additionally, the platform highlights win percentage trends over 7-day, 30-day, and 90-day windows. This helps you determine whether recent adjustments to your model are making a genuine impact or just adding noise.
Another key metric to watch is your prediction accuracy compared to market odds. BettorEdge's transparent pricing makes it easy to evaluate how your predictions stack up, especially when traditional sportsbooks often have holds of 4-6% on major props and even higher - 10-15% - on exotic ones.
Learning from Community Insights
Beyond raw data, BettorEdge's social features provide valuable context and help you benchmark your model against seasoned bettors. For instance, if you notice a group of experienced users consistently betting the under on a player's rebounding prop, dig deeper. You might uncover something your model missed, like the opposing team adding a backup center who’s a strong rebounder - a factor historical stats alone wouldn’t reveal.
Use leaderboards to measure your performance against top bettors. If your 30-day ROI sits at 4% while the top 10 users average 12%, study their strategies through the social feed. Are they avoiding certain types of props? Are they placing bets earlier or later in the day when lines might be more favorable? These insights can help you identify blind spots in your approach.
You can also join BettorEdge groups centered on specific sports or props. These communities offer a wealth of knowledge that can inspire new features or adjustments for your next model iteration.
Iterative Model Improvement
Combining performance metrics with community insights strengthens the process of refining your model. Use your tracked results to adjust parameters. For example, if your predictions frequently overestimate player performance in back-to-back games, consider adding a fatigue adjustment. Similarly, if your road game projections are less accurate than those for home games, give more weight to venue-based factors.
Make changes methodically - adjust one variable, track 50-100 bets, and then evaluate the results. BettorEdge's bet tracking tools make this process straightforward by organizing your betting history for easy review.
For a competitive edge, try setting up head-to-head challenges with other model builders in your groups. Compare your predictions directly with theirs over a set period, like a month. If their model consistently outperforms yours on a specific prop type, it’s a clear signal of where you can improve. The platform’s transparent win/loss records keep everyone accountable, encouraging a collaborative yet competitive environment for growth.
Conclusion
Creating a custom prediction model for player props is more than just crunching numbers - it’s about spotting hidden opportunities in a less-scrutinized market. Player prop markets tend to be less refined than major game lines since sportsbooks allocate fewer resources to them, leaving gaps for savvy bettors who invest time and effort into research. With the right mix of data, thoughtful feature selection, and a solid testing strategy, you can identify mispriced lines and turn those insights into profitable outcomes.
But success requires discipline. Beating the -110 odds means achieving a win rate above 52.4%. That’s no small feat, and it demands a model built on accurate data rather than gut feelings. Focus on long-term trends instead of recent streaks, factor in key variables like matchups and usage rates, and always compare your projections across multiple sportsbooks to find the most favorable lines. This methodical approach lays the groundwork for consistent, data-driven betting.
Platforms like BettorEdge offer tools to take your model further. Its analytics allow you to test, track, and refine your predictions in real time. You can evaluate your performance by league and bet type, pinpointing where your model shines and where it falls short. Plus, the platform’s social features enhance the learning curve - engage with experienced bettors, join specialized groups, or participate in head-to-head challenges to accelerate your growth.
Start simple: build a basic model, track over 100 bets, and refine your strategy using clear insights from your results. As Eric Pauly, Co-Founder & COO at BetSmart, explains:
"Smarter betting isn't about winning every night - it's about avoiding dumb losses and playing a high-volume, +EV game over time".
FAQs
How can I adjust player prop predictions for specific game factors?
To sharpen your player prop predictions for specific games, it’s essential to dive into game-specific variables. Start with factors like opponent defensive stats, pace of play, and matchup trends - think about how a player typically performs against certain teams or in particular situations. Don’t forget to consider external influences like injuries, rest days, travel fatigue, and even venue conditions (especially for outdoor sports). You can integrate these elements into your model with features such as "home vs. away" or "back-to-back games."
It’s also smart to prepare for game scenarios that could skew averages, such as blowouts or foul trouble. By using tools like probability distributions or simulations (like Monte Carlo methods), you can estimate how often these disruptions might occur and adjust your predictions to reflect these possibilities.
Leveraging technology can make this process even smoother. Tools like Python or analytics platforms can automate these adjustments, while platforms like BettorEdge offer advanced dashboards to compare your projections against market odds, track your performance, and refine your strategy over time. By layering in these considerations, your predictions can better adapt to the unique dynamics of each game.
What are the best tools for beginners to create player prop prediction models?
For those just starting out, using tools that are easy to learn can make all the difference. Python is a great option, especially with libraries like Pandas for data cleaning and Scikit-learn for basic modeling. Another solid choice is R, which is equally free, well-supported, and works seamlessly with sports data APIs. If coding feels overwhelming, don’t worry - there are alternatives. Tools like Tableau and Power BI let you analyze trends, create forecasts, and visualize player stats through simple drag-and-drop features, no coding required.
Another helpful resource is the BettorEdge platform. It offers built-in dashboards where you can upload your model results, compare them against community odds, and track your performance by league or prop type. By combining these tools, you can create a straightforward workflow: gather your data → clean and analyze it using Python or R → visualize results in Tableau or Power BI → validate your findings on BettorEdge. It’s an efficient and budget-friendly way to get started.
How can BettorEdge analytics help me improve my player prop betting strategy?
BettorEdge offers advanced analytics on bet history that turn player stats into practical insights, giving you a clear edge in decision-making. With tools to integrate metrics like points per game, rest days, injury updates, and matchup data into your own models, the platform helps you uncover patterns and pinpoint opportunities. Plus, it visualizes trends and highlights outliers, making complex data easier to interpret. One standout feature? BettorEdge offers social betting, revealing potential market mispricing in player props.
On top of that, BettorEdge keeps track of every bet you place, breaking down your performance by league, bet type, and time frame. This detailed tracking helps you identify what’s working, refine your strategies, and steer clear of common betting pitfalls. By combining custom projections, market analysis, and in-depth performance insights, BettorEdge equips you to create a smarter and more consistent betting approach.
